Performance of genetic programming to extract the trend in noisy data series

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Borrelli:2006:PhysicaA,
  author =       "A. Borrelli and I. {De Falco} and 
                 A. {Della Cioppa} and M. Nicodemi and G. Trautteur",
  title =        "Performance of genetic programming to extract the
                 trend in noisy data series",
  journal =      "Physica A: Statistical and Theoretical Physics",
  year =         "2006",
  volume =       "370",
  number =       "1",
  pages =        "104--108",
  month =        "1 " # oct,
  note =         "Econophysics Colloquium - Proceedings of the
                 International Conference {"}Econophysics
                 Colloquium{"}",
  keywords =     "genetic algorithms, genetic programming,
                 Multiobjective genetic programming, Stochastic time
                 series",
  DOI =          "doi:10.1016/j.physa.2006.04.025",
  abstract =     "In this paper an approach based on genetic programming
                 for forecasting stochastic time series is outlined. To
                 obtain a suitable test-bed some well-known time series
                 are dressed with noise. The GP approach is endowed with
                 a multiobjective scheme relying on statistical
                 properties of the faced series, i.e., on their momenta.
                 Finally, the method is applied to the MIB30 Index
                 series.",
}

Genetic Programming entries for Antonio Borrelli Ivanoe De Falco Antonio Della Cioppa Mario Nicodemi G Trautteur

Citations